GI [1]}, [2]} automatically improves upon a given solution using GP [3]}, [4]}, [5]}. This approach is inspired by the process of natural selection [6]}, in which the idea of relative fitness advantage allows for the preservation of favorable variations and guides the passing of genetic information to the next generation. The GA [7]}, [8]} is a powerful search algorithm that can efficiently find the near-optimal solution in a large search space. LGP [9]} is a special application of its variant, Genetic Programming, in which the genetic information of each individual codes for active elements in the population represented in a sequential order. The Shackleton Frameworkhttps://github.com/ARM-software/Shackleton-Framework is a generalized LGP framework that allows the use of GP on any user-defined object types and fitness metrics [10]}.
